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Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics.
The protein data bank
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne · 2000
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Scoring function for automated assessment of protein structure template quality
Yang Zhang and Jeffrey Skolnick · 2004
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Tm-align: a protein structure alignment algorithm based on the tm-score
Yang Zhang and Jeffrey Skolnick · 2005
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Biopython: freely available python tools for computational molecular biology and bioinformatics
Peter JA Cock, Tiago Antao, Jeffrey T Chang, Brad A Chapman, Cymon J Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, et al · 2009
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Imgt®, the international immunogenetics information system®
Marie-Paule Lefranc, Veronique Giudicelli, Chantal Ginestoux, Joumana Jabado-Michaloud, Geraldine Folch, Fatena Bellahcene, Yan Wu, Elodie Gemrot, Xavier Brochet, Jeroˆme Lane, et al · 2009
Earlier work this paper cites.
Atomic-level characterization of the structural dynamics of proteins
David E Shaw, Paul Maragakis, Kresten Lindorff-Larsen, Stefano Piana, Ron O Dror, Michael P Eastwood, Joseph A Bank, John M Jumper, John K Salmon, Yibing Shan, et al · 2010
Earlier work this paper cites.
SAbDab: the structural antibody database
James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy Georges, Jiye Shi, and Charlotte M. Deane · 2013
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lddt: a local superposition-free score for comparing protein structures and models using distance difference tests
Valerio Mariani, Marco Biasini, Alessandro Barbato, and Torsten Schwede · 2013
Earlier work this paper cites.
The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew J O’Meara, Frank P DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram K Mulligan, Kalli Kappel, et al · 2017
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 2017
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Rosettaantibodydesign (rabd): A general framework for computational antibody design
Jared Adolf-Bryfogle, Oleks Kalyuzhniy, Michael Kubitz, Brian D Weitzner, Xiaozhen Hu, Yumiko Adachi, William R Schief, and Roland L Dunbrack Jr · 2018
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M. Church · 2019
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Advances in protein structure prediction and design
Brian Kuhlman and Philip Bradley · 2019
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2020
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2021
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Continuous automated model evaluation (cameo)—perspectives on the future of fully automated evaluation of structure prediction methods
Xavier Robin, Juergen Haas, Rafal Gumienny, Anna Smolinski, Gerardo Tauriello, and Torsten Schwede · 2021
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Deciphering antibody affinity maturation with language models and weakly supervised learning
Jeffrey A Ruffolo, Jeffrey J Gray, and Jeremias Sulam · 2021
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OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M Stepniewska-Dziubinska, Shang Zhang, Adegoke Ojewole, Murat Efe Guney, Stella Biderman, Andrew M Watkins, Stephen Ra, Pablo Ribalta Lorenzo, Lucas Nivon, Brian Weitzner, Yih-En Andrew Ban, Peter K Sorger, Emad Mostaque, Zhao Zhang, Richard Bonneau, and Mohammed AlQuraishi · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, and D. Baker · 2022
Earlier work this paper cites.
Sampling alternative conformational states of transporters and receptors with alphafold2
Diego Del Alamo, Davide Sala, Hassane S Mchaourab, and Jens Meiler · 2022
Earlier work this paper cites.
Pifold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, Pablo Chacón, and Stan Z Li · 2022
Earlier work this paper cites.
Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, om Sercu, Adam Lerer, and Alexander Rives · 2022
Cited alongside, same era.
Antibody-antigen docking and design via hierarchical structure refinement
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2022
Cited alongside, same era.
Conditional antibody design as 3d equivariant graph translation
Xiangzhe Kong, Wenbing Huang, and Yang Liu · 2022
Cited alongside, same era.
Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
Cited alongside, same era.
Colabfold: making protein folding accessible to all
Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger · 2022
Cited alongside, same era.
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Jeffrey A Ruffolo, Lee-Shin Chu, Sai Pooja Mahajan, and Jeffrey J Gray · 2023
Later among the works it cites.
Pdb-struct: A comprehensive benchmark for structure-based protein design
Chuanrui Wang, Bozitao Zhong, Zuobai Zhang, Narendra Chaudhary, Sanchit Misra, and Jian Tang · 2023
Later among the works it cites.
Fast protein backbone generation with se (3) flow matching
Jason Yim, Andrew Campbell, Andrew YK Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S Veeling, Regina Barzilay, Tommi Jaakkola, et al · 2023
Later among the works it cites.
Structure-informed language models are protein designers
Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, and Quanquan Gu · 2023
Later among the works it cites.
Accurate structure prediction of biomolecular interactions with alphafold 3
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Tadeo Saldaño, Nahuel Escobedo, Julia Marchetti, Diego Javier Zea, Juan Mac Donagh, Ana Julia Velez Rueda, Eduardo Gonik, Agustina García Melani, Julieta Novomisky Nechcoff, Martín N Salas, et al · 2022
Cited alongside, same era.
Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola · 2022
Cited alongside, same era.
Foldseek: fast and accurate protein structure search
Michel van Kempen, Stephanie S Kim, Charlotte Tumescheit, Milot Mirdita, Cameron LM Gilchrist, Johannes Söding, and Martin Steinegger · 2022
Cited alongside, same era.
Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, et al · 2022
Cited alongside, same era.
Language models generalize beyond natural proteins
Robert Verkuil, Ori Kabeli, Yilun Du, Basile IM Wicky, Lukas F Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, and Alexander Rives · 2022
Cited alongside, same era.
Protein generation with evolutionary diffusion: sequence is all you need
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex X Lu, Nicolo Fusi, Ava P Amini, and Kevin K Yang · 2023
Cited alongside, same era.
Efficient and accurate prediction of protein structure using rosettafold2
Minkyung Baek, Ivan Anishchenko, Ian R Humphreys, Qian Cong, David Baker, and Frank DiMaio · 2023
Cited alongside, same era.
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al · 2024
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An all-atom protein generative model
Alexander E Chu, Jinho Kim, Lucy Cheng, Gina El Nesr, Minkai Xu, Richard W Shuai, and Po-Ssu Huang · 2024
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Proteininvbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics
Zhangyang Gao, Cheng Tan, Yijie Zhang, Xingran Chen, Lirong Wu, and Stan Z Li · 2024
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Simulating 500 million years of evolution with a language model
Tomas Hayes, Roshan Rao, Halil Akin, Nicholas J Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q Tran, Jonathan Deaton, Marius Wiggert, et al · 2024
Closest in time.
Efficient evolution of human antibodies from general protein language models
Brian L. Hie, Varun R. Shanker, Duo Xu, Theodora U. J. Bruun, Payton A. Weidenbacher, Shaogeng Tang, Wesley Wu, John E. Pak, and Peter S. Kim · 2024
Closest in time.
Alphafold meets flow matching for generating protein ensembles
Bowen Jing, Bonnie Berger, and Tommi Jaakkola · 2024
Closest in time.
Generalized biomolecular modeling and design with rosettafold all-atom
Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, Gyu Rie Lee, Felix S. Morey-Burrows, Ivan Anishchenko, Ian R. Humphreys, Ryan McHugh, Dionne Vafeados, Xinting Li, George A. Sutherland, Andrew Hitchcock, C. Neil Hunter, Alex Kang, Evans Brackenbrough, Asim K. Bera, Minkyung Baek, Frank DiMaio, and David Baker · 2024
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Str2str: A score-based framework for zero-shot protein conformation sampling
Jiarui Lu, Bozitao Zhong, Zuobai Zhang, and Jian Tang · 2024
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Proteingym: Large-scale benchmarks for protein fitness prediction and design
Pascal Notin, Aaron Kollasch, Daniel Ritter, Lood Van Niekerk, Steffanie Paul, Han Spinner, Nathan Rollins, Ada Shaw, Rose Orenbuch, Ruben Weitzman, et al · 2024
Closest in time.
Accurate and robust protein sequence design with carbondesign
Milong Ren, Chungong Yu, Dongbo Bu, and Haicang Zhang · 2024
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Atlas: protein flexibility description from atomistic molecular dynamics simulations
Yann Vander Meersche, Gabriel Cretin, Aria Gheeraert, Jean-Christophe Gelly, and Tatiana Galochkina · 2024
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Proteus: exploring protein structure generation for enhanced designability and efficiency
Chentong Wang, Yannan Qu, Zhangzhi Peng, Yukai Wang, Hongli Zhu, Dachuan Chen, and Longxing Cao · 2024
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Predicting multiple conformations via sequence clustering and alphafold2
Hannah K Wayment-Steele, Adedolapo Ojoawo, Renee Otten, Julia M Apitz, Warintra Pitsawong, Marc Hömberger, Sergey Ovchinnikov, Lucy Colwell, and Dorothee Kern · 2024
Closest in time.
Improved motif-scaffolding with se (3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu, Andrew YK Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S Veeling, Frank Noé, et al · 2024
Closest in time.
Predicting equilibrium distributions for molecular systems with deep learning
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, et al · 2024
Closest in time.
Antigen-specific antibody design via direct energy-based preference optimization
Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, and Quanquan Gu · 2024
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Protein design and variant prediction using autoregressive generative models
Jung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew C. Kruse, and Debora S. Marks · 2041
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